Preprint arXiv: 2204.03454 Submitted on 7 Apr 2022

We propose a variational quantum algorithm to study the real time dynamics of quantum systems as a ground-state problem. The method is based on the original proposal of Feynman and Kitaev to encode time into a register of auxiliary qubits. We prepare the Feynman-Kitaev Hamiltonian acting on the composed system as a qubit operator and find an approximate ground state using the Variational Quantum Eigensolver. We apply the algorithm to the study of the dynamics of a transverse field Ising chain with an increasing number of spins and time steps, proving a favorable scaling in terms of the number of two qubit gates. Through numerical experiments, we investigate its robustness against noise, showing that the method can be use to evaluate dynamical properties of quantum systems and detect the presence of dynamical quantum phase transitions by measuring Loschmidt echoes

Role of stochastic noise and generalization error in the time propagation of neural-network quantum states

Neural-network quantum states (NQS) have been shown to be a suitable variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional systems using time-dependent variational Monte Carlo (t-VMC). In particular, stable and accurate time propagation over long time scales has been observed in the square-lattice Heisenberg model using the Restricted Boltzmann machine architecture. However, achieving similar performance in other systems has proven to be more challenging. In this article, we focus on the two-leg Heisenberg ladder driven out of equilibrium by a pulsed excitation as a benchmark system. We demonstrate that unmitigated noise is strongly amplified by the nonlinear equations of motion for the network parameters, which causes numerical instabilities in the time evolution. As a consequence, the achievable accuracy of the simulated dynamics is a result of the interplay between network expressiveness and measures required to remedy these instabilities. We show that stability can be greatly improved by appropriate choice of regularization. This is particularly useful as tuning of the regularization typically imposes no additional computational cost. Inspired by machine learning practice, we propose a validation-set based diagnostic tool to help determining optimal regularization hyperparameters for t-VMC based propagation schemes. For our benchmark, we show that stable and accurate time propagation can be achieved in regimes of sufficiently regularized variational dynamics

Can translation invariant systems exhibit a Many-Body Localized phase?

This note is based on a talk by one of us, F. H., at the conference PSPDE II, Minho 2013. We review some of our recent works related to (the possibility of) Many-Body Localization in the absence of quenched disorder (in particular arXiv:1305.5127,arXiv:1308.6263,arXiv:1405.3279). In these works, we provide arguments why systems without quenched disorder can exhibit `asymptotic' localization, but not genuine localization.Comment: To appear in the Proceedings of the conference Particle systems and PDE's - II, held at the Center of Mathematics of the University of Minho in December 201

On the origin of the non-detection of metastable HeI in the upper atmosphere of the hot Jupiter WASP-80b

We aim to narrow down the origin of the non-detection of the metastable HeI triplet at about 10830 A obtained for the hot Jupiter WASP-80b. We measure the X-ray flux of WASP-80 from archival observations and use it as input to scaling relations accounting for the coronal [Fe/O] abundance ratio to infer the extreme-ultraviolet (EUV) flux in the 200-504 A range, which controls the formation of metastable HeI. We run three dimensional (magneto) hydrodynamic simulations of the expanding planetary upper atmosphere interacting with the stellar wind to study the impact on the HeI absorption of the stellar high-energy emission, the He/H abundance ratio, the stellar wind, and the possible presence of a planetary magnetic field up to 1 G. For a low stellar EUV emission, which is favoured by the measured logR'HK value, the HeI non-detection can be explained by a solar He/H abundance ratio in combination with a strong stellar wind, or by a sub-solar He/H abundance ratio, or by a combination of the two. For a high stellar EUV emission, the non-detection implies a sub-solar He/H abundance ratio. A planetary magnetic field is unlikely to be the cause of the non-detection. The low EUV stellar flux, driven by the low [Fe/O] coronal abundance, is the likely primary cause of the HeI non-detection. High-quality EUV spectra of nearby stars are urgently needed to improve the accuracy of high-energy emission estimates, which would then enable one to employ the observations to constrain the planetary He/H abundance ratio and the stellar wind strength. This would greatly enhance the information that can be extracted from HeI atmospheric characterisation observations.Comment: Accepted for publication on A&A, 20 page

Machine learning and the physical sciences

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant new feature is the possibility to define arbitrary neural network ansätze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation. NetKet 3 also comes with support for GPU and TPU accelerators, advanced support for discrete symmetry groups, chunking to scale up to thousands of degrees of freedom, drivers for quantum dynamics applications, and improved modularity, allowing users to use only parts of the toolbox as a foundation for their own code

Modifying and Supplementing Annie\u27s Project to Increase Impact in New Jersey and Beyond

Annie\u27s Project is a widely known risk management program emphasizing five areas of risk and creating support networks for women in agriculture. Designed as an 18-hr course delivered through a series of face-to-face classes, it can be adapted to meet the learning needs and time constraints of the target audience and instructors. This article describes modifications and additions to the traditional program delivery that were implemented by the Annie\u27s Project New Jersey team: synchronous learning at multiple locations, archived video-recorded classes, condensed 1-day workshops, a supplemental program about estate and farm transition planning, archived webinars, and international adaptations of the program

Lines and continuum sky emission in the near infrared: observational constraints from deep high spectral resolution spectra with GIANO-TNG

Aims Determining the intensity of lines and continuum airglow emission in the H-band is important for the design of faint-object infrared spectrographs. Existing spectra at low/medium resolution cannot disentangle the true sky-continuum from instrumental effects (e.g. diffuse light in the wings of strong lines). We aim to obtain, for the first time, a high resolution infrared spectrum deep enough to set significant constraints on the continuum emission between the lines in the H-band. Methods During the second commissioning run of the GIANO high-resolution infrared spectrograph at La Palma Observatory, we pointed the instrument directly to the sky and obtained a deep spectrum that extends from 0.97 to 2.4 micron. Results The spectrum shows about 1500 emission lines, a factor of two more than in previous works. Of these, 80% are identified as OH transitions; half of these are from highly excited molecules (hot-OH component) that are not included in the OH airglow emission models normally used for astronomical applications. The other lines are attributable to O2 or unidentified. Several of the faint lines are in spectral regions that were previously believed to be free of line emission. The continuum in the H-band is marginally detected at a level of about 300 photons/m^2/s/arcsec^2/micron, equivalent to 20.1 AB-mag/arcsec^2. The observed spectrum and the list of observed sky-lines are published in electronic format. Conclusions Our measurements indicate that the sky continuum in the H-band could be even darker than previously believed. However, the myriad of airglow emission lines severely limits the spectral ranges where very low background can be effectively achieved with low/medium resolution spectrographs. We identify a few spectral bands that could still remain quite dark at the resolving power foreseen for VLT-MOONS (R ~6,600).Comment: 7 pages, 4 figures, to be published in Astronomy & Astrophysic

Atmospheric mass loss and stellar wind effects in young and old systems I: comparative 3D study of TOI-942 and TOI-421 systems

Stars and planetary system

Atmospheric mass loss and stellar wind effects in young and old systems II: is TOI-942 the past of TOI-421 system?

Stars and planetary system